OpenRaiser/Intern-Atlas huggingface dataset:
Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretation
Venue: arXiv cs.CV 2024
Year: 2024

A comprehensive and reliable survival prediction model is of great importance to assist in the personalized management of Head and Neck Cancer (HNC) patients treated with curative Radiation Therapy (RT). In this work, we propose IMLSP, an Interpretable Multi-Label multi-modal deep Survival Prediction framework for predicting multiple HNC survival outcomes simultaneously and provide time-event specific visual explanation of the deep prediction process. We adopt Multi-Task Logistic Regression (MTLR) layers to convert survival prediction from a regression problem to a multi-time point classification task, and to enable predicting of multiple relevant survival outcomes at the same time. We also present Grad-TEAM, a Gradient-weighted Time-Event Activation Mapping approach specifically developed for deep survival model visual explanation, to generate patient-specific time-to-event activation maps. We evaluate our method with the publicly available RADCURE HNC dataset, where it outperforms the corresponding single-modal models and single-label models on all survival outcomes. The generated activation maps show that the model focuses primarily on the tumor and nodal volumes when making the decision and the volume of interest varies for high- and low-risk patients. We demonstrate that the multi-label learning strategy can improve the learning efficiency and prognostic performance, while the interpretable survival prediction model is promising to help understand the decision-making process of AI and facilitate personalized treatment.
Multi-Scale Dilated Convolution Network for Long-Term Time Series Forecasting
Venue: arXiv cs.LG 2024
Year: 2024

Accurate forecasting of long-term time series has important applications for decision making and planning. However, it remains challenging to capture the long-term dependencies in time series data. To better extract long-term dependencies, We propose Multi Scale Dilated Convolution Network (MSDCN), a method that utilizes a shallow dilated convolution architecture to capture the period and trend characteristics of long time series. We design different convolution blocks with exponentially growing dilations and varying kernel sizes to sample time series data at different scales. Furthermore, we utilize traditional autoregressive model to capture the linear relationships within the data. To validate the effectiveness of the proposed approach, we conduct experiments on eight challenging long-term time series forecasting benchmark datasets. The experimental results show that our approach outperforms the prior state-of-the-art approaches and shows significant inference speed improvements compared to several strong baseline methods.
Generative Model for Joint Resource Management in Multi-Cell Multi-Carrier NOMA Networks
Venue: arXiv cs.NI 2024
Year: 2024

In this work, we design a generative artificial intelligence (GAI) -based framework for joint resource allocation, beamforming, and power allocation in multi-cell multi-carrier non-orthogonal multiple access (NOMA) networks. We formulate the proposed problem as sum rate maximization problem. Next, we design a novel multi-task transformer (MTT) framework to handle the problem in real-time. To provide the necessary training set, we consider simplified but powerful mathematical techniques from the literature. Then, we train and test the proposed MTT. We perform simulation to evaluate the efficiency of the proposed MTT and compare its performance with the mathematical baseline.
Robust Pseudo-label Learning with Neighbor Relation for Unsupervised Visible-Infrared Person Re-Identification
Venue: arXiv cs.CV 2024
Year: 2024

Unsupervised Visible-Infrared Person Re-identification (USVI-ReID) presents a formidable challenge, which aims to match pedestrian images across visible and infrared modalities without any annotations. Recently, clustered pseudo-label methods have become predominant in USVI-ReID, although the inherent noise in pseudo-labels presents a significant obstacle. Most existing works primarily focus on shielding the model from the harmful effects of noise, neglecting to calibrate noisy pseudo-labels usually associated with hard samples, which will compromise the robustness of the model. To address this issue, we design a Robust Pseudo-label Learning with Neighbor Relation (RPNR) framework for USVI-ReID. To be specific, we first introduce a straightforward yet potent Noisy Pseudo-label Calibration module to correct noisy pseudo-labels. Due to the high intra-class variations, noisy pseudo-labels are difficult to calibrate completely. Therefore, we introduce a Neighbor Relation Learning module to reduce high intra-class variations by modeling potential interactions between all samples. Subsequently, we devise an Optimal Transport Prototype Matching module to establish reliable cross-modality correspondences. On that basis, we design a Memory Hybrid Learning module to jointly learn modality-specific and modality-invariant information. Comprehensive experiments conducted on two widely recognized benchmarks, SYSU-MM01 and RegDB, demonstrate that RPNR outperforms the current state-of-the-art GUR with an average Rank-1 improvement of 10.3%. The source codes will be released soon.
SwapTalk: Audio-Driven Talking Face Generation with One-Shot Customization in Latent Space
Venue: arXiv cs.CV 2024
Year: 2024

Combining face swapping with lip synchronization technology offers a cost-effective solution for customized talking face generation. However, directly cascading existing models together tends to introduce significant interference between tasks and reduce video clarity because the interaction space is limited to the low-level semantic RGB space. To address this issue, we propose an innovative unified framework, SwapTalk, which accomplishes both face swapping and lip synchronization tasks in the same latent space. Referring to recent work on face generation, we choose the VQ-embedding space due to its excellent editability and fidelity performance. To enhance the framework's generalization capabilities for unseen identities, we incorporate identity loss during the training of the face swapping module. Additionally, we introduce expert discriminator supervision within the latent space during the training of the lip synchronization module to elevate synchronization quality. In the evaluation phase, previous studies primarily focused on the self-reconstruction of lip movements in synchronous audio-visual videos. To better approximate real-world applications, we expand the evaluation scope to asynchronous audio-video scenarios. Furthermore, we introduce a novel identity consistency metric to more comprehensively assess the identity consistency over time series in generated facial videos. Experimental results on the HDTF demonstrate that our method significantly surpasses existing techniques in video quality, lip synchronization accuracy, face swapping fidelity, and identity consistency. Our demo is available at http://swaptalk.cc.
Passive Obstacle Aware Control to Follow Desired Velocities
Venue: arXiv cs.RO 2024
Year: 2024

Evaluating and updating the obstacle avoidance velocity for an autonomous robot in real-time ensures robustness against noise and disturbances. A passive damping controller can obtain the desired motion with a torque-controlled robot, which remains compliant and ensures a safe response to external perturbations. Here, we propose a novel approach for designing the passive control policy. Our algorithm complies with obstacle-free zones while transitioning to increased damping near obstacles to ensure collision avoidance. This approach ensures stability across diverse scenarios, effectively mitigating disturbances. Validation on a 7DoF robot arm demonstrates superior collision rejection capabilities compared to the baseline, underlining its practicality for real-world applications. Our obstacle-aware damping controller represents a substantial advancement in secure robot control within complex and uncertain environments.
Between proof construction and SAT-solving
Venue: arXiv cs.LO 2024
Year: 2024

The classical satisfiability problem (SAT) is used as a natural and general tool to express and solve combinatorial problems that are in NP. We postulate that provability for implicational intuitionistic propositional logic (IIPC) can serve as a similar natural tool to express problems in Pspace. This approach can be particularly convenient for two reasons. One is that provability in full IPC (with all connectives) can be reduced to provability of implicational formulas of order three. Another advantage is a convenient interpretation in terms of simple alternating automata. Additionally, we distinguish some natural subclasses of IIPC corresponding to the complexity classes NP and co-NP. Our experimental results show that a simple decision procedure requires a significant amount of time only in a small fraction of cases.
Imprecise Multi-Armed Bandits
Venue: arXiv cs.LG 2024
Year: 2024

We introduce a novel multi-armed bandit framework, where each arm is associated with a fixed unknown credal set over the space of outcomes (which can be richer than just the reward). The arm-to-credal-set correspondence comes from a known class of hypotheses. We then define a notion of regret corresponding to the lower prevision defined by these credal sets. Equivalently, the setting can be regarded as a two-player zero-sum game, where, on each round, the agent chooses an arm and the adversary chooses the distribution over outcomes from a set of options associated with this arm. The regret is defined with respect to the value of game. For certain natural hypothesis classes, loosely analgous to stochastic linear bandits (which are a special case of the resulting setting), we propose an algorithm and prove a corresponding upper bound on regret. We also prove lower bounds on regret for particular special cases.
Optimal Baseline Corrections for Off-Policy Contextual Bandits
Venue: arXiv cs.LG 2024
Year: 2024

The off-policy learning paradigm allows for recommender systems and general ranking applications to be framed as decision-making problems, where we aim to learn decision policies that optimize an unbiased offline estimate of an online reward metric. With unbiasedness comes potentially high variance, and prevalent methods exist to reduce estimation variance. These methods typically make use of control variates, either additive (i.e., baseline corrections or doubly robust methods) or multiplicative (i.e., self-normalisation). Our work unifies these approaches by proposing a single framework built on their equivalence in learning scenarios. The foundation of our framework is the derivation of an equivalent baseline correction for all of the existing control variates. Consequently, our framework enables us to characterize the variance-optimal unbiased estimator and provide a closed-form solution for it. This optimal estimator brings significantly improved performance in both evaluation and learning, and minimizes data requirements. Empirical observations corroborate our theoretical findings.
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we propose a No-Reference Image Quality Assessment (NRIQA) guided cut-off point selection (CPS) strategy to enhance the performance of a fine-grained classification system. Scores given by existing NRIQA methods on the same image may vary and not be as independent of natural image augmentations as expected, which weakens their connection and explainability to fine-grained image classification. Taking the three most commonly adopted image augmentation configurations -- cropping, rotating, and blurring -- as the entry point, we formulate a two-step mechanism for selecting the most discriminative subset from a given image dataset by considering both the confidence of model predictions and the density distribution of image qualities over several NRIQA methods. Concretely, the cut-off points yielded by those methods are aggregated via majority voting to inform the process of image subset selection. The efficacy and efficiency of such a mechanism have been confirmed by comparing the models being trained on high-quality images against a combination of high- and low-quality ones, with a range of 0.7% to 4.2% improvement on a commercial product dataset in terms of mean accuracy through four deep neural classifiers. The robustness of the mechanism has been proven by the observations that all the selected high-quality images can work jointly with 70% low-quality images with 1.3% of classification precision sacrificed when using ResNet34 in an ablation study.
DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

Detail features of magnetic resonance images play a cru-cial role in accurate medical diagnosis and treatment, as they capture subtle changes that pose challenges for doc-tors when performing precise judgments. However, the widely utilized naive diffusion model has limitations, as it fails to accurately capture more intricate details. To en-hance the quality of MRI reconstruction, we propose a comprehensive detail-preserving reconstruction method using multiple diffusion models to extract structure and detail features in k-space domain instead of image do-main. Moreover, virtual binary modal masks are utilized to refine the range of values in k-space data through highly adaptive center windows, which allows the model to focus its attention more efficiently. Last but not least, an inverted pyramid structure is employed, where the top-down image information gradually decreases, ena-bling a cascade representation. The framework effective-ly represents multi-scale sampled data, taking into ac-count the sparsity of the inverted pyramid architecture, and utilizes cascade training data distribution to repre-sent multi-scale data. Through a step-by-step refinement approach, the method refines the approximation of de-tails. Finally, the proposed method was evaluated by con-ducting experiments on clinical and public datasets. The results demonstrate that the proposed method outper-forms other methods.
Experimental Pragmatics with Machines: Testing LLM Predictions for the Inferences of Plain and Embedded Disjunctions
Venue: arXiv cs.CL 2024
Year: 2024

Human communication is based on a variety of inferences that we draw from sentences, often going beyond what is literally said. While there is wide agreement on the basic distinction between entailment, implicature, and presupposition, the status of many inferences remains controversial. In this paper, we focus on three inferences of plain and embedded disjunctions, and compare them with regular scalar implicatures. We investigate this comparison from the novel perspective of the predictions of state-of-the-art large language models, using the same experimental paradigms as recent studies investigating the same inferences with humans. The results of our best performing models mostly align with those of humans, both in the large differences we find between those inferences and implicatures, as well as in fine-grained distinctions among different aspects of those inferences.
Towards a More Inclusive AI: Progress and Perspectives in Large Language Model Training for the Sámi Language
Venue: arXiv cs.CL 2024
Year: 2024

Sámi, an indigenous language group comprising multiple languages, faces digital marginalization due to the limited availability of data and sophisticated language models designed for its linguistic intricacies. This work focuses on increasing technological participation for the Sámi language. We draw the attention of the ML community towards the language modeling problem of Ultra Low Resource (ULR) languages. ULR languages are those for which the amount of available textual resources is very low, and the speaker count for them is also very low. ULRLs are also not supported by mainstream Large Language Models (LLMs) like ChatGPT, due to which gathering artificial training data for them becomes even more challenging. Mainstream AI foundational model development has given less attention to this category of languages. Generally, these languages have very few speakers, making it hard to find them. However, it is important to develop foundational models for these ULR languages to promote inclusion and the tangible abilities and impact of LLMs. To this end, we have compiled the available Sámi language resources from the web to create a clean dataset for training language models. In order to study the behavior of modern LLM models with ULR languages (Sámi), we have experimented with different kinds of LLMs, mainly at the order of $\sim$ seven billion parameters. We have also explored the effect of multilingual LLM training for ULRLs. We found that the decoder-only models under a sequential multilingual training scenario perform better than joint multilingual training, whereas multilingual training with high semantic overlap, in general, performs better than training from scratch.This is the first study on the Sámi language for adapting non-statistical language models that use the latest developments in the field of natural language processing (NLP).
Neural Network Learning of Black-Scholes Equation for Option Pricing
Venue: arXiv cs.LG 2024
Year: 2024

One of the most discussed problems in the financial world is stock option pricing. The Black-Scholes Equation is a Parabolic Partial Differential Equation which provides an option pricing model. The present work proposes an approach based on Neural Networks to solve the Black-Scholes Equations. Real-world data from the stock options market were used as the initial boundary to solve the Black-Scholes Equation. In particular, times series of call options prices of Brazilian companies Petrobras and Vale were employed. The results indicate that the network can learn to solve the Black-Sholes Equation for a specific real-world stock options time series. The experimental results showed that the Neural network option pricing based on the Black-Sholes Equation solution can reach an option pricing forecasting more accurate than the traditional Black-Sholes analytical solutions. The experimental results making it possible to use this methodology to make short-term call option price forecasts in options markets.
FusionTransNet for Smart Urban Mobility: Spatiotemporal Traffic Forecasting Through Multimodal Network Integration
Venue: arXiv cs.LG 2024
Year: 2024

This study develops FusionTransNet, a framework designed for Origin-Destination (OD) flow predictions within smart and multimodal urban transportation systems. Urban transportation complexity arises from the spatiotemporal interactions among various traffic modes. Motivated by analyzing multimodal data from Shenzhen, a framework that can dissect complicated spatiotemporal interactions between these modes, from the microscopic local level to the macroscopic city-wide perspective, is essential. The framework contains three core components: the Intra-modal Learning Module, the Inter-modal Learning Module, and the Prediction Decoder. The Intra-modal Learning Module is designed to analyze spatial dependencies within individual transportation modes, facilitating a granular understanding of single-mode spatiotemporal dynamics. The Inter-modal Learning Module extends this analysis, integrating data across different modes to uncover cross-modal interdependencies, by breaking down the interactions at both local and global scales. Finally, the Prediction Decoder synthesizes insights from the preceding modules to generate accurate OD flow predictions, translating complex multimodal interactions into forecasts. Empirical evaluations conducted in metropolitan contexts, including Shenzhen and New York, demonstrate FusionTransNet's superior predictive accuracy compared to existing state-of-the-art methods. The implication of this study extends beyond urban transportation, as the method for transferring information across different spatiotemporal graphs at both local and global scales can be instrumental in other spatial systems, such as supply chain logistics and epidemics spreading.
A Robust eLORETA Technique for Localization of Brain Sources in the Presence of Forward Model Uncertainties
Venue: arXiv cs.CE 2024
Year: 2024

In this paper, we present a robust version of the well-known exact low-resolution electromagnetic tomography (eLORETA) technique, named ReLORETA, to localize brain sources in the presence of different forward model uncertainties. Methods: We first assume that the true lead field matrix is a transformation of the existing lead field matrix distorted by uncertainties and propose an iterative approach to estimate this transformation accurately. Major sources of the forward model uncertainties, including differences in geometry, conductivity, and source space resolution between the real and simulated head models, and misaligned electrode positions, are then simulated to test the proposed method. Results: ReLORETA and eLORETA are applied to simulated focal sources in different regions of the brain and the presence of various noise levels as well as real data from a patient with focal epilepsy. The results show that ReLORETA is considerably more robust and accurate than eLORETA in all cases. Conclusion: Having successfully dealt with the forward model uncertainties, ReLORETA proved to be a promising method for real-world clinical applications. Significance: eLORETA is one of the localization techniques that could be used to study brain activity for medical applications such as determining the epileptogenic zone in patients with medically refractory epilepsy. However, the major limitation of eLORETA is sensitivity to the uncertainties in the forward model. Since this problem can substantially undermine its performance in real-world applications where the exact lead field matrix is unknown, developing a more robust method capable of dealing with these uncertainties is of significant interest.
Enhancing Suicide Risk Detection on Social Media through Semi-Supervised Deep Label Smoothing
Venue: arXiv cs.LG 2024
Year: 2024

Suicide is a prominent issue in society. Unfortunately, many people at risk for suicide do not receive the support required. Barriers to people receiving support include social stigma and lack of access to mental health care. With the popularity of social media, people have turned to online forums, such as Reddit to express their feelings and seek support. This provides the opportunity to support people with the aid of artificial intelligence. Social media posts can be classified, using text classification, to help connect people with professional help. However, these systems fail to account for the inherent uncertainty in classifying mental health conditions. Unlike other areas of healthcare, mental health conditions have no objective measurements of disease often relying on expert opinion. Thus when formulating deep learning problems involving mental health, using hard, binary labels does not accurately represent the true nature of the data. In these settings, where human experts may disagree, fuzzy or soft labels may be more appropriate. The current work introduces a novel label smoothing method which we use to capture any uncertainty within the data. We test our approach on a five-label multi-class classification problem. We show, our semi-supervised deep label smoothing method improves classification accuracy above the existing state of the art. Where existing research reports an accuracy of 43\% on the Reddit C-SSRS dataset, using empirical experiments to evaluate our novel label smoothing method, we improve upon this existing benchmark to 52\%. These improvements in model performance have the potential to better support those experiencing mental distress. Future work should explore the use of probabilistic methods in both natural language processing and quantifying contributions of both epistemic and aleatoric uncertainty in noisy datasets.